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Robust causal inference with continuous instruments using the local instrumental variable curve

Author

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  • Edward H. Kennedy
  • Scott Lorch
  • Dylan S. Small

Abstract

Instrumental variables are commonly used to estimate effects of a treatment afflicted by unmeasured confounding, and in practice instruments are often continuous (e.g. measures of distance, or treatment preference). However, available methods for continuous instruments have important limitations: they either require restrictive parametric assumptions for identification, or else rely on modelling both the outcome and the treatment process well (and require modelling effect modification by all adjustment covariates). In this work we develop the first semiparametric doubly robust estimators of the local instrumental variable effect curve, i.e. the effect among those who would take treatment for instrument values above some threshold and not below. In addition to being robust to misspecification of either the instrument or treatment or outcome processes, our approach also incorporates information about the instrument mechanism and allows for flexible data‐adaptive estimation of effect modification. We discuss asymptotic properties under weak conditions and use the methods to study infant mortality effects of neonatal intensive care units with high versus low technical capacity, using travel time as an instrument.

Suggested Citation

  • Edward H. Kennedy & Scott Lorch & Dylan S. Small, 2019. "Robust causal inference with continuous instruments using the local instrumental variable curve," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(1), pages 121-143, February.
  • Handle: RePEc:bla:jorssb:v:81:y:2019:i:1:p:121-143
    DOI: 10.1111/rssb.12300
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    Cited by:

    1. Wan, Siyu & Lee, Yoong Hon & Sarma, Vengadeshvaran J., 2023. "Is Fintech good for green finance? Empirical evidence from listed banks in China," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1273-1291.
    2. Sönke Hendrik Matthewes & Guglielmo Ventura, 2022. "On Track to Success? Returns to vocational education against different alternatives," CVER Research Papers 038, Centre for Vocational Education Research.
    3. Stijn Vansteelandt & Oliver Dukes, 2022. "Assumption‐lean inference for generalised linear model parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 657-685, July.
    4. Ting Ye & Ashkan Ertefaie & James Flory & Sean Hennessy & Dylan S. Small, 2023. "Instrumented difference‐in‐differences," Biometrics, The International Biometric Society, vol. 79(2), pages 569-581, June.
    5. Kerda Varaku & Robin Sickles, 2023. "Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks," Empirical Economics, Springer, vol. 64(6), pages 3121-3165, June.
    6. Kim Kwangho & Kennedy Edward H. & Naimi Ashley I., 2021. "Incremental intervention effects in studies with dropout and many timepoints#," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 302-344, January.
    7. Jann Spiess & Vasilis Syrgkanis & Victor Yaneng Wang, 2021. "Finding Subgroups with Significant Treatment Effects," Papers 2103.07066, arXiv.org, revised Dec 2023.
    8. Yumou Qiu & Jing Tao & Xiao‐Hua Zhou, 2021. "Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 1016-1043, November.
    9. Maria Cuellar & Edward H. Kennedy, 2020. "A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1793-1818, October.
    10. Sönke Hendrik Matthewes & Guglielmo Ventura, 2022. "On Track to Success? Returns to Vocational Education Against Different Alternatives," CEPA Discussion Papers 58, Center for Economic Policy Analysis.
    11. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    12. Ariadna García-Prado & Paula González & Yolanda F. Rebollo-Sanz, 2024. "Confinement policies: controlling contagion without compromising mental health," Working Papers 24.03, Universidad Pablo de Olavide, Department of Economics.

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